Equilibrium tuned by a magnetic field in phase separated manganite

We present magnetic and transport measurements on La5/8-yPryCa3/8MnO3 with y = 0.3, a manganite compound exhibiting intrinsic multiphase coexistence of sub-micrometric ferromagnetic and antiferromagnetic charge ordered regions. Time relaxation effects between 60 and 120K, and the obtained magnetic and resistive viscosities, unveils the dynamic nature of the phase separated state. An experimental procedure based on the derivative of the time relaxation after the application and removal of a magnetic field enables the determination of the otherwise unreachable equilibrium state of the phase separated system. With this procedure the equilibrium phase fraction for zero field as a function of temperature is obtained. The presented results allow a correlation between the distance of the system to the equilibrium state and its relaxation behavior.Comment: 13 pages, 5 figures. Submited to Journal of Physics: Condensed Matte

Witness-based Approach for Scaling Distributed Ledgers to Massive IoT Scenarios

Distributed Ledger Technologies (DLTs) are playing a major role in building security and trust in Internet of Things (IoT) systems. However, IoT deployments with a large number of devices, such as in environment monitoring applications, generate and send massive amounts of data. This would generate vast number of transactions that must be processed within the distributed ledger. In this work, we first demonstrate that the Proof of Work (PoW) blockchain fails to scale in a sizable IoT connectivity infrastructure. To solve this problem, we present a lightweight distributed ledger scheme to integrate PoW blockchain into IoT. In our scheme, we classify transactions into two types: 1) global transactions, which must be processed by global blockchain nodes and 2) local transactions, which can be processed locally by entities called witnesses. Performance evaluation demonstrates that our proposed scheme improves the scalability of integrated blockchain and IoT monitoring systems by processing a fraction of the transactions, inversely proportional to the number of witnesses, locally. Hence, reducing the number of global transactions.Comment: 6 pages, 7 figures, conference pape

Predictors of fatigue severity in early systemic sclerosis: a prospective longitudinal study of the GENISOS cohort.

ObjectivesLongitudinal studies examining the baseline predictors of fatigue in SSc have not been reported. Our objectives were to examine the course of fatigue severity over time and to identify baseline clinical, demographic, and psychosocial predictors of sequentially obtained fatigue scores in early SSc. We also examined baseline predictors of change in fatigue severity over time.MethodsWe analyzed 1090 longitudinal Fatigue Severity Scale (FSS) scores belonging to 256 patients who were enrolled in the Genetics versus Environment in Scleroderma Outcomes Study (GENISOS). Predictive significance of baseline variables for sequentially obtained FSS scores was examined with generalized linear mixed models. Predictors of change in FSS over time were examined by adding an interaction term between the baseline variable and time-in-study to the model.ResultsThe patients' mean age was 48.6 years, 47% were Caucasians, and 59% had diffuse cutaneous involvement. The mean disease duration at enrollment was 2.5 years. The FSS was obtained at enrollment and follow-up visits (mean follow-up time = 3.8 years). Average baseline FSS score was 4.7(±0.96). The FSS was relatively stable and did not show a consistent trend for change over time (p = 0.221). In a multivariable model of objective clinical variables, higher Medsger Gastrointestinal (p = 0.006) and Joint (p = 0.024) Severity Indices, and anti-U1-RNP antibodies (p = 0.024) were independent predictors of higher FSS. In the final model, ineffective coping skills captured by higher Illness Behavior Questionnaire scores (p<0.001), higher self-reported pain (p = 0.006), and higher Medsger Gastrointestinal Severity Index (p = 0.009) at enrollment were independent predictors of higher longitudinal FSS scores. Baseline DLco % predicted was the only independent variable that significantly predicted a change in FSS scores over time (p = 0.013), with lower DLco levels predicting an increase in FSS over time.ConclusionsThis study identified potentially modifiable clinical and psychological factors that predict longitudinal fatigue severity in early SSc

Synchronization interfaces and overlapping communities in complex networks

We show that a complex network of phase oscillators may display interfaces between domains (clusters) of synchronized oscillations. The emergence and dynamics of these interfaces are studied in the general framework of interacting phase oscillators composed of either dynamical domains (influenced by different forcing processes), or structural domains (modular networks). The obtained results allow to give a functional definition of overlapping structures in modular networks, and suggest a practical method to identify them. As a result, our algorithm could detect information on both single overlapping nodes and overlapping clusters.Comment: 5 pages, 4 figure

Trusted Wireless Monitoring based on Blockchain over NB-IoT Connectivity

The data collected from Internet of Things (IoT) devices on various emissions or pollution, can have a significant economic value for the stakeholders. This makes it prone to abuse or tampering and brings forward the need to integrate IoT with a Distributed Ledger Technology (DLT) to collect, store, and protect the IoT data. However, DLT brings an additional overhead to the frugal IoT connectivity and symmetrizes the IoT traffic, thus changing the usual assumption that IoT is uplink-oriented. We have implemented a platform that integrates DLTs with a monitoring system based on narrowband IoT (NB-IoT). We evaluate the performance and discuss the tradeoffs in two use cases: data authorization and real-time monitoring.Comment: 7 pages, 6 figures, Accepted in IEEE Communication Magazin

Q-learning for distributed routing in LEO satellite constellations

End-to-end routing in Low Earth Orbit (LEO) satellite constellations (LSatCs) is a complex and dynamic problem. The topology, of finite size, is dynamic and predictable, the traffic from/to Earth and transiting the space segment is highly imbalanced, and the delay is dominated by the propagation time in non-congested routes and by the queueing time at Inter-Satellite Links (ISLs) in congested routes. Traditional routing algorithms depend on excessive communication with ground or other satellites, and oversimplify the characterization of the path links towards the destination. We model the problem as a multi-agent Partially Observable Markov Decision Problem (POMDP) where the nodes (i.e., the satellites) interact only with nearby nodes. We propose a distributed Q-learning solution that leverages on the knowledge of the neighbours and the correlation of the routing decisions of each node. We compare our results to two centralized algorithms based on the shortest path: one aiming at using the highest data rate links and a second genie algorithm that knows the instantaneous queueing delays at all satellites. The results of our proposal are positive on every front: (1) it experiences delays that are comparable to the benchmarks in steady-state conditions; (2) it increases the supported traffic load without congestion; and (3) it can be easily implemented in a LSatC as it does not depend on the ground segment and minimizes the signaling overhead among satellites